Poverty of Thought

Classify speech excerpts as POT or NO-POT using explicit ideational content criteria.

Updated Nov 18, 2025
One-click install
npx skills add https://github.com/Kikolo3000/topsy_databaseprocessing-agent --skill poverty-of-thought
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Poverty of Thought
Source: https://github.com/Kikolo3000/topsy_databaseprocessing-agent/tree/main/skills/POT
Command: npx skills add https://github.com/Kikolo3000/topsy_databaseprocessing-agent --skill poverty-of-thought

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps clinicians and researchers identify and quantify Poverty of Thought by evaluating restricted ideational content in speech.

Core Features & Use Cases

  • Structured rubric for POT with explicit criteria and examples to guide consistent labeling.
  • Versatile applications in clinical interviews, research datasets, and educational settings to benchmark cognitive language patterns.
  • Interpretable outputs suitable for integration into notes, reports, or datasets.

Quick Start

Feed a short transcript to obtain a POT/NO-POT classification with a concise rationale.

Frequently Asked Questions about Poverty of Thought

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect poverty of thought in clinical interview transcripts?▼

To detect poverty of thought in clinical interview transcripts, you classify speech excerpts by evaluating restricted ideational content and explicit thought restriction awareness to output a POT or NO-POT label with a rationale.

What is poverty of thought in speech analysis?▼

Poverty of thought in speech analysis is a language disorder indicator defined by restricted ideational content, where speech exhibits diminished ideas and explicit awareness of thought restriction, signaling potential thought disorders in clinical psychology.

Can I use automated speech analysis to quantify thought disorder presence in research datasets?▼

You can use automated speech analysis to quantify thought disorder presence in research datasets by feeding short transcript fragments into a structured rubric to yield consistent POT or NO-POT classifications.

What is the best way to classify language disorder indicators in therapy transcripts?▼

The best way to classify language disorder indicators in therapy transcripts is applying predefined explicit criteria for ideational content, ensuring outputs reflect the POT criteria and provide a rationale aligned with clinical examples.

Does this poverty of thought classification method work without clinical training?▼

This poverty of thought classification method uses a structured rubric with explicit criteria and examples to guide consistent labeling, making it interpretable for educational settings and benchmarking cognitive language patterns without requiring direct clinical training.